Computers accept human user input in various ways. One of the most common input devices is the keyboard. Additional types of input mechanisms include mice and other pointing devices. Although useful for many purposes, keyboards and mice (as well as other pointing devices) sometimes lack flexibility. For example, many persons find it easier to write, take notes, etc. with a pen and paper instead of a keyboard. Mice and other types of pointing devices do not generally provide a true substitute for pen and paper. This is especially true for cursive writing or when utilizing complex languages, such as for example, East Asian languages. As used herein, “East Asian” includes, but is not limited to, written languages such Japanese, Chinese and Korean. Written forms of these languages contain thousands of characters, and specialized keyboards for these languages can be cumbersome and require specialized training to properly use.
Electronic tablets or other types of electronic writing devices offer an attractive alternative to keyboards and mice. These devices typically include a stylus with which a user can write upon a display screen in a manner similar to using a pen and paper. A digitizer nested within the display converts movement of the stylus across the display into an “electronic ink” representation of the user's writing. The electronic ink is stored as coordinate values for a collection of points along the line(s) drawn by the user. Software may then be used to analyze the electronic ink to recognize characters, and then convert the electronic ink to Unicode, ASCII or other code values for what the user has written.
It would be highly advantageous to employ a training module to allow computing devices, such as Tablet PCs, to recognize a user's handwriting more accurately. Given the highly variable nature of handwriting and the problems identified above, recognition training is often tedious and inefficient and generally not effective. For example, handwriting samples from the same individual may be of varying types, sizes and distributions. Regarding varying types of samples, one or more sample may comprise a collection of dictionary words, phrases or sentences, telephone numbers, dates, times, people names, geographical names, web and e-mail addresses, postal addresses, numbers, formulas, single character data, or a combination thereof.